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Dynamic modeling of liquid-desiccant regenerator based on a state–space method

Author

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  • Liu, Wei
  • Gong, Yanfeng
  • Niu, Xiaofeng
  • Shen, Junjie
  • Kosonen, Risto

Abstract

The regenerator is the main energy-consuming component in a liquid-desiccant-based air-conditioning system. Highly efficient control of a regenerator in dynamic operating conditions can improve system performance and reduce system energy consumption. An accurate and reliable dynamic model of a regenerator is fundamental to understand its dynamic operation characteristics and design in a high-performance control system. In this study, a state–space based model of the dynamic operation characteristics of a liquid-desiccant regenerator was developed from the control point of view. In the modeling process, the relationship between the input and output variables was reflected by constructed state–space matrixes. An experimental validation of the proposed dynamic regenerator model showed the dynamic responses of the following three case studies: (1) increase of mass flow rate of inlet desiccant solution, (2) increase in inlet desiccant solution temperature, and (3) increase in inlet air temperature and humidity. The proposed state–space model accurately reflected the dynamic response process of regeneration under different disturbances and initial conditions. Comparison between the modeling and experimental results showed that the relative errors of the outlet air temperature/humidity and outlet desiccant solution temperature were less than 5%, and the mean relative errors of their variations were less than 15%, which validates the accuracy and reliability of the proposed state–space regenerator model. This novel model can be useful for further research on the dynamic operation characteristics of regenerators and the development of high-performance control systems.

Suggested Citation

  • Liu, Wei & Gong, Yanfeng & Niu, Xiaofeng & Shen, Junjie & Kosonen, Risto, 2019. "Dynamic modeling of liquid-desiccant regenerator based on a state–space method," Applied Energy, Elsevier, vol. 240(C), pages 744-753.
  • Handle: RePEc:eee:appene:v:240:y:2019:i:c:p:744-753
    DOI: 10.1016/j.apenergy.2019.02.082
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    References listed on IDEAS

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    Cited by:

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    2. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).

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